Post on 17-Jan-2016
SEPTA FARE SENSITIVITY ANALYSISUsing DVRPC’s Regional Travel Forecasting Model
Fang Yuan, Brad Lane, and Vanvi TrieuMay 17, 2015
Outline
Introduction Fare Elasticities from the Literature Data How we model Fares at DVRPC Scenarios Analyzed Conclusions and Recommendations
Delaware Valley Regional Planning Commission
Metropolitan Planning Organization (MPO) 2 States 9 Counties 351 Municipalities 5.6 Million Population 3,800 sq. miles ~115 employees
Activities – Long Range Plan (LRP) Transportation Improvement Program (TIP) Wide range of planning and technical support for
regional partners
Introduction
Analysis was done as part of model improvement process
We have several major transit studies coming up
Really wanted to see how well our model does at capturing the impact of fare changes
Elasticity of Ridership in Literature
Fare Typically -0.33 (-0.1 to -0.6, higher in long
term) Rail/subway is less elastic (more resilient) than
bus Peak-hour is less elastic than off-peak
Population (+0.61) and employment (+0.25) Service (+0.71) Gas price (+0.12 ~ +0.16) Trip type and user type Parking availability/cost and auto ownership
Data
Time period: 2000 – 2014 A lot of changes in Philadelphia Gathered data on:
Fares Employment Population Gas Prices Ridership
Data - Fares
200020012002200320042005200620072008200920102011201220132014 $-
$0.50
$1.00
$1.50
$2.00
$2.50
Cash FareAdult TokenMonthly TransPass/64Transfer Ticket
Year
Fa
re P
ric
e
SEPTA Fare Price History (2000 – 2014)
Data – Employment
Percent Annual Change in Employment
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
-5.0%
-4.0%
-3.0%
-2.0%
-1.0%
0.0%
1.0%
2.0%
3.0%
DVRPC RegionUnited States
Year
Em
plo
ym
en
t P
erc
en
t C
ha
ng
e
Data – Unemployment
Unemployment Rate - Philadelphia-Camden-Wilmington MSA
20002000200120022003200420052006200720082009201020112011201220130.0%
2.0%
4.0%
6.0%
8.0%
10.0%
Year
Un
em
plo
ym
en
t R
ate
Data - Population
2000 2002 2004 2006 2008 2010 2012 20144.0
4.4
4.8
5.2
5.6
6.0
1.0
1.2
1.4
1.6
1.8
2.0
DVRPC RegionPhiladelphia
Year
DV
RP
C R
eg
ion
Po
pu
lati
on
(M
illio
n)
Ph
ilad
elp
hia
Po
pu
lati
on
(M
illio
n)
Census Population (2000 – 2013)
Data – Gas Prices
Retail Price of Gasoline - Central Atlantic Region
2000200020012002200320042005200620072008200920102011201120122013 $-
$1.00
$2.00
$3.00
$4.00
$5.00
Adjusted for Inflation
Not Adjusted
Year
Ga
so
line
Pri
ce
Data - Ridership
20002001
20022003
20042005
20062007
20082009
20102011
20122013
270
290
310
330
350
Fiscal Year
To
tal R
ide
rsh
ip (
Mill
ion
)
Total SEPTA Ridership (2000 – 2013)
Data – Summary 2000 to 2014 Fares – Increasing Employment –
Sharp Drop during Recession, then slowly, steadily coming back
Population – Steady increase for Region as a whole City - Beginning in 2009, first uptick in decades
Gas Prices – Sharp Drop during Recession Then climbed back
Ridership – Despite (or because of) above - Increasing
How we model Fares
SEPTA has a very complex fare structure And their ridership and revenue data–by
their own admission–it’s not great Our trip based model (TIM 2.0) and
VISUM need “aggregate” fare inputs
A major challenge is just to model the existing fare system
How we model Fares
SEPTA has a very complex fare structure
Transit Fare Modeling TIM 2.1
Line –> Fare System
Stop –> Fare Zone
Transit Fare Modeling TIM 2.1
Fare System–> Base fare
Bus – zone based
Regional Rail – zone-to-zone based
Transit Fare Modeling TIM 2.1
Fare System–> Transfer discount
2010 Average Fare – SEPTA City Bus
Fare Media Fare Cost
Rides per Fare Media
Per-Ride Fare
Weight by Riders
Weighted Fare
Adult Token $1.55 1 $1.55 18.3% $0.28
Cash Fare $2.00 1 $2.00 15.4% $0.31
Monthly TransPass $83.00 64 $1.30 14.2% $0.18
Weekly TransPass $22.00 17 $1.30 26.6% $0.34
Senior Citizen $1.00 1 $0.00 11.6% $0.00
School Ride $15.36 9 $1.77 11.7% $0.21
Day Pass $7.00 7 $1.00 0.7% $0.01
Handicap Fare $1.00 1 $1.00 1.0% $0.01
Free Ride $0.00 1 $0.00 0.6% $0.00
Average Fare — — — — $1.34
Model Calibration – FY 2011 Daily Ridership
Transit System
FY 2011 Count
Model Output
Difference
%Difference
City Rail 418,420 367,471 −50,949 −12.2%
City Bus 468,355 508,701 40,346 8.6%
Victory 56,744 65,022 8,278 14.6%
Frontier 13,489 20,732 7,243 53.7%
Regional Rail 118,305 113,947 −4,358 −3.7%
SEPTA Total 1,075,313 1,075,873 560 0.1%
PATCO Total 35,686 37,000 1,314 3.7%
NJT Total 83,402 73,739 −9,663 −11.6%
Region-Wide Total 1,194,401 1,186,612 −7,789 −0.7%
Scenarios Analyzed
Direct Elasticity Test - Hypothetical Fare Changes
Cross Elasticity Test - Hypothetical Fare Changes
Backcast and Validation - July 2010 Fare Change
Forecast and Validation - July 2013 Fare Change
Forecast - Impact of New Payment Technology
Scenario 1: Direct Elasticity Test
-5% 0% 5% 10% 15% 20%-16%
-14%
-12%
-10%
-8%
-6%
-4%
-2%
0%
2%
City RailCity BusVictory TransitFrontier TransitRegional Rail
Hypothetical Fare Change
Rid
ers
hip
Ch
an
ge
Scenario 2: 2010 Fare Change
July 2010 Fare Change Adult token +7% Transfer ticket +33% TransPass +6% TrailPass +5~10%
Gas Price +28% (2010-11) Modeled as distance-based toll
Modeling Scenario Fare and gas price change No population/employment/service
change
Transit System
Average Fare Increase Per Leg
City Rail $ 0.04 4%
City Bus $ 0.03 3%
Victory $ 0.07 7%
Frontier $ 0.06 5%Regional Rail(All Zone Pairs)
$ 0.09 3%
Model vs. Count – before and after 2010 Fare Change
Transit System
SEPTA Count Model ResultsDifferen
ce%Differen
ceDifferen
ce%Differen
ceCity Rail 11,335 2.8% 2,746 0.8%
City Bus 15,054 3.3% 9,465 1.9%
Victory 3,104 5.8% 389 0.6%
Frontier 690 5.4% 570 2.8%
Regional Rail 3,280 2.9% −1,959 −1.7%
Total 33,463 3.2% 11,210 1.1%
Scenario 3 – 2013 Fare Change
July 2013 Fare Change Adult token +16% Cash fare +13% Transfer ticket +0%, TransPass +9% Fare Zone changes
Gas Price Stabilized (2011-14) Population/Household/Employment +1%
(2010-14) Modeling Scenario
Fare and population/employment change No other changes
Transit System
Average Fare Increase Per
Leg
City Rail $ 0.06 6%
City Bus $ 0.05 6%
Victory $ 0.04 4%
Frontier $ 0.06 5%Regional Rail(All Zone Pairs)
$ 0.17 6%
Model vs. Count – before and after 2013 Fare Change
Transit System
SEPTA Counts Model ResultsDifferen
ce%Differen
ceDifferen
ce%Differen
ceCity Rail 3,536 0.8% 6,075 1.7%
City Bus 27,622 5.9% 17,617 3.5%
Victory 2,854 5.0% 1,181 1.8%
Frontier 69 0.5% 261 1.3%
Regional Rail 10,510 8.9% −1,031 −0.9%
Total 44,592 4.1% 24,102 2.2%
Conclusions and Recommendations TIM 2.1 performed well in estimating the
impact of fare changes (and simultaneous changes of multiple factors) on ridership change
Revisit the model configuration given the relatively high Regional Rail fare sensitivity
Include sensitivity test and backcasting exercise as a part of the TIM 3.0 (ABM) validation
under $25,000
$25,000 to $34,999
$35,000 to $49,999
$50,000 to $74,999
$75,000 to $99,999
$100,000 to $149,999
$150,000 to $199,999
$200,000+0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
City BusRegional Rail
Income Comparison – City Bus Passenger vs. Regional Rail Passenger